Lead the design and implementation of data pipelines (ETL/ELT) for both batch and real-time processing. Build and optimize data lakes and data warehouses to support enterprise analytics and AI/ML use cases. Define and implement data modeling standards, metadata management, and master data management (MDM) event-driven and streaming solutions using Kafka, EventBridge, and similar technologies. Ensure compliance with governance, data security, privacy, and regulatory requirements (HIPAA, GDPR, etc. ). Drive adoption of best practices in data engineering, cloud architecture, and DevOps for data platforms

Collaborate with business stakeholders, data scientists, engineers, and product teams to deliver data-driven insights.

technical leadership, mentoring, and guidance to engineering teams.

Required Skills & Experience :

10+ years of experience in data architecture, engineering, or related technical roles Proven expertise in the modern data stack : AWS Glue, Lambda, Kinesis, S3, Redshift/Snowflake. Strong programming and scripting experience with SQL and Python Hands-on experience with workflow orchestration tools (Airflow, Prefect, Step Functions). Proficiency in Big Data technologies : Spark, PySpark, Scala. Solid understanding of streaming & event-driven architectures /Event Bus). Experience in data modeling, building data lakes/warehouses , and architecting analytical platforms. Strong knowledge of data governance, security, compliance, and MDM Excellent problem-solving, analytical, and system design skills. Exceptional communication, stakeholder management , and leadership capabilities.

Nice-to-Have Skills :

Exposure to ML Ops and supporting AI/ML pipelines. Experience with containerization and orchestration (Docker, Kubernetes). Familiarity with CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation).

Data Architect

Job Description

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